Some interesting method like style transfer, GAN, deep neural networks for Chinese character and calligraphic image processing
Some interesting method like style transfer, GAN, deep neural networks for Chinese character and calligraphic image processing
Loss | Test accuracy | Confusion matrix |
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Content image dataset: http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar
The method of this application, we just simply use pix2pix to generate another style of Chinese character.
Dataset: https://pan.baidu.com/s/1JagVbA8p-Bn5OnoOErJAyQ extract code: 2vku
These great calligraphy works are written by my teacher Prof. Zhang.
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[4]. Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution[C]//European conference on computer vision. Springer, Cham, 2016: 694-711.
[1]. Style transfer for calligraphic image: https://github.com/MingtaoGuo/Conditional-Instance-Norm-for-n-Style-Transfer
[2]. zi2zi: https://github.com/MingtaoGuo/DCGAN_WGAN_WGAN-GP_LSGAN_SNGAN_RSGAN_BEGAN_ACGAN_PGGAN_TensorFlow
[3]. Calligraphic image denoising: https://github.com/MingtaoGuo/Calligraphic-Images-Denoising-by-GAN